Workflow and convolutional neural network for automated identification of animal sounds

Ruff, Zachary J.; Lesmeister, Damon B.; Appel, Cara L.; Sullivan, Christopher

doi:10.1016/j.ecolind.2021.107419

Cited by 56 publications

(37 citation statements)

References 32 publications

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“…These observations can be linked to remote sensing data to describe and then generate hypotheses about the relationships and effects of forest structure, abiotic factors, and anthropogenic disturbance. This approach would not be possible without the technological advancements that enable long-term passive acoustic monitoring of forests, hundreds of terabytes of acoustic data to be affordably stored and archived, and the advent of high-performance computers and artificial intelligence to automate the classification of hundreds of thousands of hours of acoustic recordings (Ruff et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Whereas an increased efficiency of data collection would generally constrain the storage, processing, analysis, and sharing of large datasets, advances in artificial intelligence (e.g., computer vision and deep learning), and high-performance computers have demonstrated the capability to relieve some of the bottlenecks in data management and processing. In addition, these advances make these technologies more accessible to ecologists because they are no longer cost prohibitive, limited to hardware that is physically accessible (e.g., cloud computing, remote computing, and virtual networks), or reliant on proprietary software for analyses (i.e., open-source software such as Program R) (Ruff et al, 2021).…”

Section: What Is In Nature and Where Is It: Devices To Detect Species Individuals And Communitiesmentioning

confidence: 99%

“…(C) Animals can also now be counted from space, including animals as small as albatross, which Dolliver (2019) counted using imagery from WorldView-3. (D) Bioacoustics is now being used to detect Spotted Owls, Barred Owls, and myriad other species (Ruff et al, 2021) as part of Federal monitoring responsibilities under the Northwest Forest Plan. This will provide information about species occurrence, and migratory phenology of songbirds, from Central California to the Canadian border.…”

Section: Advances In Genetics: Describing Nature With Environmental Dna High-throughput Sequencing and Dna Metabarcodingmentioning

confidence: 99%

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The Rapid Rise of Next-Generation Natural History

et al. 2021

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Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.

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Section: Discussionmentioning

confidence: 99%

Section: What Is In Nature and Where Is It: Devices To Detect Species Individuals And Communitiesmentioning

confidence: 99%

Section: Advances In Genetics: Describing Nature With Environmental Dna High-throughput Sequencing and Dna Metabarcodingmentioning

confidence: 99%

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The Rapid Rise of Next-Generation Natural History

et al. 2021

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“…Error backpropagation will be activated when the output deviates too much from the expected output to adjust the network weight and threshold according to the prediction error. In this way, the predicted output of BPNN approximates the expected output infinitely until the iteration is completed (Ruff et al, 2021). The algorithm flow of BPNN is first network training and then prediction.…”

Section: Artificial Intelligence Technologymentioning

confidence: 99%

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Chen

Song²,

Yuan³

et al. 2022

Front. Psychol.

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This work intends to solve the problem that the traditional education system cannot reasonably adjust the educational integration of children with the arrival of labor force in a short time, and support the education of migrant children (MC) in the education policy (EP) to integrate them into the local educational environment as soon as possible. Firstly, this work defines the surplus labor force and MC. Secondly, the principles of Artificial Intelligence (AI) and Deep Learning (DL) are introduced. Thirdly, it analyzes the education of MC and relevant policies, and the data of the education effect of MC are collected and the evaluation effect model is built. Finally, the evaluation model of MC’s education effect is applied to test the effect of EP. The results show that using AI technology combined with DL technology to model the education effect of MC can establish an effective and accurate evaluation model of the education effect of MC, effectively evaluate the impact of local education policies on the education of MC, and give an effective effect analysis of relevant education policies in each period. The result of Adaptive Resonance Theory (ART)–Back Propagation algorithm is 65 ∼ 96%, which is much higher than the efficiency of traditional algorithms. This shows that the education integration evaluation model of MC based on AI technology and DL technology can effectively and accurately evaluate the integration effect of MC on the local education system, and then provide reference for local and even national adjustment of education policies. The results provide a new idea for the application of new technology in EP.

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“…A CNN uses a series of learned filters to extract features of interest from various inputs and can be applied for ACR from raw sound files (Knight et al 2017, Ruff et al 2020). Although CNNs have been used for extracting calls of interest while limiting false positives for a multitude of avian, mammalian, and aquatic species (Knight et al 2017, Zhong et al 2020, Premoli et al 2021, Ruff et al 2021), to our knowledge they have yet to be developed for identifying gobbles of male wild turkeys ( Meleagris gallopavo ).…”

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confidence: 99%

Comparison of methods for automated identification of wild turkey gobbles

Wightman

Henrichs

Collier

et al. 2022

Wildlife Society Bulletin

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Autonomous recording units (ARUs) allow for collection of extensive acoustic data sets, while reducing costs and time associated with traditional surveys used to determine gobbling chronology of male wild turkeys (Meleagris gallopavo). A challenge with ARUs is efficiently locating and identifying calls of interest, so autonomous call recognition (ACR) software such as Raven Pro have traditionally been used to identify wild turkey gobbles. However, ACR software often produces high false positive detections, requiring substantive time to verify selections as gobbles. We used ARUs across 3 study sites in the southeastern United States to collect 107,580 hours of ambient sound. We developed a convolutional neural network (CNN) to autonomously identify wild turkey gobbles and compared results of our CNN to results gathered using the commercially available program Raven Pro. After processing of ambient sound, the CNN detected 15,793 more gobbles than Raven Pro, and did so with 5,716,718 fewer selections. Collectively, our CNN improved precision from 0.01 to 0.32 relative to Raven Pro, while decreasing the time required for validation from 4,452 hours to 219. We found precision of our CNN varied across ARUs primarily due to differences in occurrence of ambient sounds similar to gobbles. Thus, we recommend that additional site-specific training data should be considered when developing CNNs. Our results suggest that researchers interested in describing gobbling activity by male wild turkeys should consider developing and applying CNNs for automated call recognition.

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Workflow and convolutional neural network for automated identification of animal sounds

Cited by 56 publications

References 32 publications

The Rapid Rise of Next-Generation Natural History

The Rapid Rise of Next-Generation Natural History

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Comparison of methods for automated identification of wild turkey gobbles

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